The present invention relates to field of health management systems. More specifically, the present invention provides for identifying episodes of care and measuring the severity of an episode.
Measures of episodes of care may be used to set capitation rates or to profile clinicians' performance. Numerous approaches to measuring episodes of care exist. Examples include Prospective Risk Adjustment, Ambulatory Visit Groups, Disease Staging, Products of Ambulatory Care, Ambulatory Diagnosis Groups and Ambulatory Care Groups. In addition to broad approaches to measurement of episodes of illness, many have developed disease specific episodes of care.
Three problems exist with the current approaches to measuring episodes of care. First, no current approach provides a mathematical model for measuring episodes of care. Most existing approaches to measuring episodes of care do not describe the internal procedures used for measuring severity or identifying episodes of care. Some commercial approaches seem to consider such information as business secrets that and do not disclosed internal procedures. Even when they do describe the internal mechanism of their approach, all appear to rely on heuristics that make clinical sense but do not provide a mathematical theory for the relation between the variables used in constructing episodes of care. Thus, researchers face a black box—the content of which they know little about or may be based on heuristics that they cannot easily modify and reapply. In the absence of a theory, it is difficult to learn from one study how better measures can be constructed. Each study and each approach exists on its own merits and fails to contribute to the other. Researchers then tend to compete on claims of accuracy rather than to build on each other's work. As a result, while many approaches exist, there is little cumulative progress in the field. The ability of one investigator to build on another person's approach has been limited. What is needed is a mathematical theory that allows for the accumulation of information to improve our understanding of how severity of episodes of care should be measured. Then, future researchers may change be able to modify or change theories to arrive at predictions that are more accurate. Theories may be modified and knowledge accumulated as new insights are found.
Second, current approaches do not allow for identifying episodes of care without first classifying diagnoses into clusters of diseases. All existing approaches are built on the concept of classifying possible diagnoses into a few clusters and then findings rules for creating episodes for these clusters. Schneeweiss and colleagues in an article entitles “Diagnostic clusters: A new tool for analyzing the content of ambulatory medical records,” in Medical Care 1983, XXI (1): 105-122, reported that 92 diagnosis clusters make up 86 percent of all ambulatory visits. Others have expanded this set to 125, with varying levels of severity and different periods of time, during which the diagnoses in the cluster belongs to the same episode. What is needed is an approach that does not attempt to reduce the large set of possible diagnoses into a smaller set of clusters. Reductionist approaches, by definition, give up important nuances in order to have a manageable set of diagnoses. For example, infections often follow wounds and therefore may be considered part of the same episode. But an otitis media, even though an infection of the ear, could not possibly be part of an episode of trauma to the leg. Defining all infections as one cluster of diagnoses forces investigators to ignore important differences that might exist between types of infections. It may be important that operations are defined on individual diagnoses without need to pre-set diagnoses into broad clusters. Sometimes classification of diseases into clusters is based on the etiology of the disease, leading to possible counter intuitive classifications. An episode of trauma may include a fracture to the knee as well as a fracture of the leg, even though the knee fracture and leg fracture are different problems. Similarly, congestive heart failure may be part of an episode of myocardial infarction even though one involves the heart the other the lung. Two very dissimilar diagnoses may be part of the same episode, even though these diagnoses do not describe the same illness.
Third, many current approaches create homogenous resource use episodes. Not all follow-up visits are part of the same episode even though they may all be short visits and therefore have similar resource use. The nature of the diagnosis, not the intensity of visits should be the basis of classifying visits into episodes. For example, follow-up visit for myocardial infarction is part of an MI episode and a follow-up visit for trauma is part of trauma episode. Intensity-based measures may not be used for evaluating whether the numbers of visits are appropriate. In essence, they are fee schedules, except that these fee schedules are based on groups of visits or diagnoses and not single visit diagnosis. What is also needed is a relation-based episode classification system that remedies this important shortcoming. A elation-based episode classification system may be used to judge appropriateness of number of visits.
Efficient healthcare management requires accurately tracking the diagnosis and care of illness beyond what is currently in use. What is needed is a relation-based episode classification system that allows for the accumulation of information to improve the understanding of how severity of episodes of care may be measured without reducing the large set of possible diagnoses into a smaller set of clusters.